Assignment 

This is a python assignment which has 2 parts, the python coding part and the final report part. Some work has been done on it, but it needs to be rechecked, modify the coding and write up a report for this assignment.

Task:

Analyze and write report on the extent and nature of the injury or violence problems for
– “Kyrgystan”

  1. Introduction:

Introduce and discuss relevant features/facts of your country.

  1. Datasets:
    a. Discuss the datasets you’ve used and any relevant details which are required to understand how you’ve extracted your country’s data. Examples of relevant information for the WHO database would be:
    i. the WHO Mortality Database country code for your selected country,
    ii. the ICD files, Years and Lists used in your analysis,
    iii. a table which summarizes the code and causes of death descriptions for your country’s leading causes of death, as discussed in your report,
    iv. a table which summarizes which causes of death codes you classified as a death due to injury or violence.
  2. Analysis and Discussion:
    In this section you will analyze and discuss the extent and nature of the injury or violence problem for your selected country. You are required to provide graphs and tables, as indicated below, to support and illustrate your analysis and discussion. You may also include additional graphs and tables.

This section should provide analysis and discussion to address the following questions:
a. What are the current leading causes of injury deaths in your country?
i. Provide a pie chart, which displays the top 5 leading causes of deaths due to injuries and violence based on your country’s most current year’s data. Include the remainder of the injury and violence deaths as other.
b. Have injury deaths risen in rank over the last thirty years?
i. Provide tables which compare the top twenty causes of all deaths (not just due to injuries and violence) over the last thirty years, in fifteen yearincrements starting from the most current year’s data and going backwards. For example, if your country has data from 1950 to 2010 you will have a table displaying the top 20 causes of all deaths for the years 1980, 1995 and 2010.
ii. Provide a time series chart depicting how the top ten current leading causes of injury and deaths have changed over the last thirty years. Your data should be in five yearincrements. For example, if your country has data from 1950 to 2010, your chart will include the years: 1980, 1985, 1990, 1995, 2000, 2005 and 2010.
c. Are some groups more vulnerable to injuries and violence than others?
i. Provide a vertical bar chart which displays the death rates by cause of injury and group for the most current year’s data. Use the top five current leading causes of injury and deaths (from part b), and the following groups: youths (ages 15-29, any gender), males (all ages) and females (all ages).
d. Does poverty increase the risk of injury?
i. Compare your country’s deaths due to injury and violence to another country in your world region which has a different WHO income level classification. For example, per the provided LMIC-HIC_country_grouping document, Australia’s WHO region code is Wpr HI, indicating that Australia is in the West pacific region and is considered High income. So a good comparison country for Australia could be Vanuatu, with a WHO region of Wpr LMI.

For each of your countries, Provide an appropriate chart with the top twenty causes of all deaths (not just due to injuries and violence) based on the most recent years data which is available for both countries. For example, if the most recent year’s data for one country is 2011, and the other’s is 2009, you should use the 2009 data for both countries.
ii. Provide a vertical bar chart displaying the percentage of all deaths due to injury and violence for both countries.


  • Conclusion:

    a. Summarize the main points discussed in your report, including your findings for the leading causes of death due to injury and violence for your country.
  • References:
    All sources must be referenced, but since Data Science is a broad multi-disciplinary field I leave the choice of referencing style up to you. The style you choose must be used consistently for all in-text references and all in-text references must be included in your list of references. 

Solution 

Final_Project.ipynb 

{

“cells”: [

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“# CSC8001: Final Project Report [50%]”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“**Name:** your name here<br>\n”,

“**ID:** 0061093885<br>”

]

},

{

“cell_type”: “code”,

“execution_count”: 1,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: [

“importnumpy as np\n”,

“import pandas as pd\n”,

“importmatplotlib.pyplot as pl\n”,

“importseaborn as sns\n”,

“%matplotlib inline”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“## Introduction [10 marks]”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“Kyrgyzstan is a Central Asian country of incredible natural beauty and proud nomadic traditions.\n”,

“Its recorded history spans over 2,000 years, encompassing a variety of cultures and empires. Although geographically isolated by its highly mountainous terrain, which has helped preserve its ancient culture, Kyrgyzstan has been at the crossroads of several great civilizations as part of the Silk Road and other commercial and cultural routes.\n”,

“\n”,

“Most of the territory of present-day Kyrgyzstan was formally annexed to the Russian Empire in 1876. \n”,

“The Kyrgyz staged a major revolt against the Tsarist Empire in 1916 in which almost one-sixth of the Kyrgyz population was killed. Kyrgyzstan became a Soviet republic in 1936 and achieved independence in 1991 when the USSR dissolved.\n”,

“\n”,

“Kyrgyzstan is a landlocked, mountainous, lower middle income country with an economy dominated by minerals extraction, agriculture, and reliance on remittances from citizens working abroad. Cotton, wool, and meat are the main agricultural products, although only cotton is exported in any quantity. Other exports include gold, mercury, uranium, natural gas, and – in some years – electricity.\n”,

“\n”,

“Kyrgyzstan’s population is estimated at 5,789,122 (July 2017 est.) with population growth rate 1.1% (2017 est.).\n”,

“Of those, 34.4% are under the age of 15 and 6.2% are over 65. The country is rural: only about 1/3 of the population \n”,

“live in urban areas. The average population density is 25 people per km².\n”,

“The vast majority of Kyrgyzstanis live in rural areas; densest population settlement is to the north in and around the capital, Bishkek, followed by Osh in the west; the least densely populated area is the east, southeast in the Tien Shan mountains.\n”,

“\n”,

“Some healthcare and population related features are prsented in a table below.\n”,

“\n”,

“| Feature | Value |\n”,

“|——————————————————————————-|————|\n”,

“| Gross national income per capita (PPP international $, 2013)                  |   3        |\n”,

“| Life expectancy at birth m/f (years, 2015)                                    |\t67/75    |\n”,

“| Probability of dying between 15 and 60 years m/f (per 1 000 population, 2015) |\t231/102  |\n”,

“| Total expenditure on health per capita (Intl \\$, 2014)                        |\t215      |\n”,

“| Total expenditure on health as % of GDP (2014)                                |\t6.5      |\n”,

“| Population below poverty line as % of population (2015 est.)                  |   32.1     |\n”,

“| Labor force  (2016 est.)                                                      | 2 778 000  |\n”,

“| Child labor (ages 5-17)                                                       |  563 920   |\n”,

“| Unemployment, % of population (youth ages 15-24)                              |   15%      |\n”,

“\n”,

“\n”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“## Datasets [10 marks]”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“WHO Mortality Data base (25 November 2015 update)\n”,

“\n”,

“The WHO Mortality Data base comprises deaths registered in national vital registration systems, \n”,

“with underlying cause of death as coded by the relevant national authority. \n”,

“Underlying cause of death is defined as “the disease or injury which initiated the train of morbid events \n”,

“leading directly to death, or the circumstances of the accident or violence which produced the fatal injury” \n”,

“in accordance with the rules of the International Classification of Diseases. \n”,

“\n”,

“The database contains number of deaths by country, year, sex, age group and cause of death as far back from 1950. \n”,

“Data are included only for countries reporting data properly coded according to the \n”,

“International Classification of Diseases (ICD).\n”,

“\n”,

“the WHO Mortality Database country code for Kyrgyzstan is 4184.\n”,

“In the analysis I used ICD 10 Morticd10_part1.csv and Morticd10_part2.csv\n”,

“which span 2000 – 2013 years range.”

]

},

{

“cell_type”: “code”,

“execution_count”: 2,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: [

“#LMI (WHO LMIC regions & HIC) = Eur \n”,

“\n”,

“country_codes = pd.read_csv(‘Project_Files/country_codes.csv’, index_col=1).to_dict()[‘country’]\n”,

“code = country_codes[‘Kyrgyzstan’]”

]

},

{

“cell_type”: “code”,

“execution_count”: 3,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: [

“all_codes = pd.read_csv(‘AllValidCodes/allvalid2011 (detailed titles headings).txt’, \n”,

”                        encoding=’latin-1′, sep=’\\t’, index_col=0, usecols=[1, 2], \n”,

”                        skiprows=6, converters={‘Code’: lambda x: x.replace(‘.’, ”)}).dropna()”

]

},

{

“cell_type”: “code”,

“execution_count”: 4,

“metadata”: {},

“outputs”: [

{

“name”: “stderr”,

“output_type”: “stream”,

“text”: [

“/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n”,

”  interactivity=interactivity, compiler=compiler, result=result)\n”,

“/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n”,

”  interactivity=interactivity, compiler=compiler, result=result)\n”,

“/usr/local/lib/python3.4/dist-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \n”,

“A value is trying to be set on a copy of a slice from a DataFrame.\n”,

“Try using .loc[row_indexer,col_indexer] = value instead\n”,

“\n”,

“See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n”

]

}

],

“source”: [

“chunks = []\n”,

“forfname in [‘Project_Files/Morticd10_part1.csv’, ‘Project_Files/Morticd10_part2.csv’]:\n”,

”    fordf in pd.read_csv(fname, index_col=0, chunksize=10**5):\n”,

”        chunk = df.loc[df.index == code, :]\n”,

”        iflen(chunk) > 0:\n”,

”            chunks.append(chunk)\n”,

“morticd = pd.concat(chunks)\n”,

“# the cause \”AAA\” refers to total deaths from all causes combined\n”,

“target_morticd = morticd[morticd[‘Cause’] != ‘AAA’]\n”,

“# we are not interested in details, only general group\n”,

“target_morticd[‘Cause’] = target_morticd[‘Cause’].str[:3]”

]

},

{

“cell_type”: “code”,

“execution_count”: 5,

“metadata”: {},

“outputs”: [

{

“data”: {

“text/plain”: [

“2013”

]

},

“execution_count”: 5,

“metadata”: {},

“output_type”: “execute_result”

}

],

“source”: [

“curr_year = target_morticd[‘Year’].max()\n”,

“curr_year”

]

},

{

“cell_type”: “code”,

“execution_count”: 28,

“metadata”: {},

“outputs”: [

{

“data”: {

“text/plain”: [

“Index([‘Deaths1’, ‘Deaths2’, ‘Deaths3’, ‘Deaths4’, ‘Deaths5’, ‘Deaths6’,\n”,

”       ‘Deaths7’, ‘Deaths8’, ‘Deaths9’, ‘Deaths10’, ‘Deaths11’, ‘Deaths12’,\n”,

”       ‘Deaths13’, ‘Deaths14’, ‘Deaths15’, ‘Deaths16’, ‘Deaths17’, ‘Deaths18’,\n”,

”       ‘Deaths19’, ‘Deaths20’, ‘Deaths21’, ‘Deaths22’, ‘Deaths23’, ‘Deaths24’,\n”,

”       ‘Deaths25’, ‘Deaths26’, ‘IM_Deaths1’, ‘IM_Deaths2’, ‘IM_Deaths3’,\n”,

”       ‘IM_Deaths4′],\n”,

”      dtype=’object’)”

]

},

“execution_count”: 28,

“metadata”: {},

“output_type”: “execute_result”

}

],

“source”: [

“target_morticd.columns[8:]”

]

},

{

“cell_type”: “code”,

“execution_count”: 34,

“metadata”: {},

“outputs”: [

{

“name”: “stdout”,

“output_type”: “stream”,

“text”: [

“20 Leading Causes of Death in Kyrgyzstan\n”

]

},

{

“data”: {

“text/html”: [

“<div>\n”,

“<style>\n”,

”    .dataframetheadtr:only-childth {\n”,

”        text-align: right;\n”,

”    }\n”,

“\n”,

”    .dataframetheadth {\n”,

”        text-align: left;\n”,

”    }\n”,

“\n”,

”    .dataframetbodytrth {\n”,

”        vertical-align: top;\n”,

”    }\n”,

“</style>\n”,

“<table border=\”1\” class=\”dataframe\”>\n”,

”  <thead>\n”,

”    <tr style=\”text-align: right;\”>\n”,

”      <th></th>\n”,

”      <th>index</th>\n”,

”      <th>ICD Title</th>\n”,

”      <th>Deaths1</th>\n”,

”    </tr>\n”,

”  </thead>\n”,

”  <tbody>\n”,

”    <tr>\n”,

”      <th>0</th>\n”,

”      <td>I25</td>\n”,

”      <td>Chronic ischemic heart disease</td>\n”,

”      <td>118796</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>1</th>\n”,

”      <td>I64</td>\n”,

”      <td>Stroke, not specified as hemorrhage or infarction</td>\n”,

”      <td>54275</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>2</th>\n”,

”      <td>K74</td>\n”,

”      <td>Fibrosis and cirrhosis of liver</td>\n”,

”      <td>24205</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>3</th>\n”,

”      <td>J44</td>\n”,

”      <td>Other chronic obstructive pulmonary disease</td>\n”,

”      <td>20570</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>4</th>\n”,

”      <td>I67</td>\n”,

”      <td>Other cerebrovascular diseases</td>\n”,

”      <td>15422</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>5</th>\n”,

”      <td>I21</td>\n”,

”      <td>Acute myocardial infarction</td>\n”,

”      <td>14266</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>6</th>\n”,

”      <td>J18</td>\n”,

”      <td>Pneumonia, organism unspecified</td>\n”,

”      <td>12146</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>7</th>\n”,

”      <td>C16</td>\n”,

”      <td>Malignant neoplasm of stomach</td>\n”,

”      <td>8002</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>8</th>\n”,

”      <td>P22</td>\n”,

”      <td>Respiratory distress of newborn</td>\n”,

”      <td>6897</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>9</th>\n”,

”      <td>I61</td>\n”,

”      <td>Intracerebral hemorrhage</td>\n”,

”      <td>6730</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>10</th>\n”,

”      <td>V89</td>\n”,

”      <td>Motor- or nonmotor-vehicle accident, type of v…</td>\n”,

”      <td>6377</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>11</th>\n”,

”      <td>X70</td>\n”,

”      <td>Intentional self-harm (suicide) by hanging, st…</td>\n”,

”      <td>6175</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>12</th>\n”,

”      <td>C34</td>\n”,

”      <td>Malignant neoplasm of bronchus and lung</td>\n”,

”      <td>5760</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>13</th>\n”,

”      <td>A16</td>\n”,

”      <td>Respiratory tuberculosis, not confirmed bacter…</td>\n”,

”      <td>5379</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>14</th>\n”,

”      <td>P21</td>\n”,

”      <td>Birth asphyxia</td>\n”,

”      <td>5033</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>15</th>\n”,

”      <td>I11</td>\n”,

”      <td>Hypertensive heart disease</td>\n”,

”      <td>4993</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>16</th>\n”,

”      <td>A15</td>\n”,

”      <td>NaN</td>\n”,

”      <td>4757</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>17</th>\n”,

”      <td>R54</td>\n”,

”      <td>Senility</td>\n”,

”      <td>4692</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>18</th>\n”,

”      <td>X45</td>\n”,

”      <td>Accidental poisoning by and exposure to alcohol</td>\n”,

”      <td>4508</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>19</th>\n”,

”      <td>N03</td>\n”,

”      <td>Chronic nephritic syndrome</td>\n”,

”      <td>3981</td>\n”,

”    </tr>\n”,

”  </tbody>\n”,

“</table>\n”,

“</div>”

],

“text/plain”: [

”   index                                          ICD Title  Deaths1\n”,

“0    I25                     Chronic ischemic heart disease   118796\n”,

“1    I64  Stroke, not specified as hemorrhage or infarction    54275\n”,

“2    K74                    Fibrosis and cirrhosis of liver    24205\n”,

“3    J44        Other chronic obstructive pulmonary disease    20570\n”,

“4    I67                     Other cerebrovascular diseases    15422\n”,

“5    I21                        Acute myocardial infarction    14266\n”,

“6    J18                    Pneumonia, organism unspecified    12146\n”,

“7    C16                      Malignant neoplasm of stomach     8002\n”,

“8    P22                    Respiratory distress of newborn     6897\n”,

“9    I61                           Intracerebral hemorrhage     6730\n”,

“10   V89  Motor- or nonmotor-vehicle accident, type of v…     6377\n”,

“11   X70  Intentional self-harm (suicide) by hanging, st…     6175\n”,

“12   C34            Malignant neoplasm of bronchus and lung     5760\n”,

“13   A16  Respiratory tuberculosis, not confirmed bacter…     5379\n”,

“14   P21                                     Birth asphyxia     5033\n”,

“15   I11                         Hypertensive heart disease     4993\n”,

“16   A15                                                NaN     4757\n”,

“17   R54                                           Senility     4692\n”,

“18   X45    Accidental poisoning by and exposure to alcohol     4508\n”,

“19   N03                       Chronic nephritic syndrome       3981”

]

},

“execution_count”: 34,

“metadata”: {},

“output_type”: “execute_result”

}

],

“source”: [

“top20 = target_morticd.iloc[:, 8].fillna(0.0).groupby(target_morticd[‘Cause’]).sum().nlargest(20)\n”,

“print(\”20 Leading Causes of Death in Kyrgyzstan\”)\n”,

“all_codes.join(top20.to_frame(), how=’right’).sort_values(by=’Deaths1′,ascending=False).reset_index()”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“#### Causes of death codes classified as a death due to injury or violence\n”,

“\n”,

“| Code    | ICD Title |\n”,

“———————————————–|\n”,

“| S00-S09 | \tInjuries to the head\t|\n”,

“| S10-S19 | \tInjuries to the neck\t\t\t\t|\n”,

“| S20-S29 | \tInjuries to the thorax\t\t\t\t\t\t\t\t\t|\n”,

“| S30-S39 | \tInjuries to the abdomen, lower back,<br> lumbar spine and pelvis|\n”,

“| S40-S49 | \tInjuries to the shoulder and upper arm\t|\n”,

“| S50-S59 | \tInjuries to the elbow and forearm\t|\n”,

“| S60-S69 | \tInjuries to the wrist and hand\t|\n”,

“| S70-S79 | \tInjuries to the hip and thigh\t|\n”,

“| S80-S89 | \tInjuries to the knee and lower leg\t|\n”,

“| S90-S99 | \tInjuries to the ankle and foot\t|\n”,

“| T00-T07 | \tInjuries involving multiple body regions\t\t|\n”,

“| T08-T14 | \tInjuries to unspecified part of trunk,<br> limb or body region |\n”,

“| V01-V09 | \tPedestrian injured in transport accident\t|\n”,

“| V10-V19 | \tPedal cyclist injured in transport accident\t\t\t|\n”,

“| V20-V29 | \tMotorcycle rider injured in transport accident\t\t\t|\n”,

“| V30-V39 | \tOccupant of three-wheeled motor vehicle <br> injured in transport accident\t\t|\n”,

“| V40-V49 | \tCar occupant injured in transport accident\t\t|\n”,

“| V50-V59 | \tOccupant of pick-up truck or van <br> injured in transport accident\t\t\t\t\t\t\t\t|\n”,

“| V60-V69 | \tOccupant of heavy transport vehicle <br> injured in transport accident\t|\n”,

“| V70-V79 | \tBus occupant injured in transport accident|\n”,

“| V80-V89 | \tOther land transport accidents\t|\n”,

“| V90-V94 | \tWater transport accidents\t|\n”,

“| V95-V97 | \tAir and space transport accidents\t\t\t|\n”,

“| V98-V99 | \tOther and unspecified transport accidents\t|\n”,

“| W00-W19 | \tFalls\t|\n”,

“| W20-W49 | \tExposure to inanimate mechanical forces\t\t|\n”,

“| W50-W64 | \tExposure to animate mechanical forces\t|\n”,

“| W65-W74 | \tAccidental drowning and submersion\t|\n”,

“| W75-W84 | \tOther accidental threats to breathing\t|\n”,

“| W85-W99 | \tExposure to electric current, radiation and extreme <br> ambient air temperature and pressure |\n”,

“| X00-X09 | \tExposure to smoke, fire and flames\t\t|\n”,

“| X10-X19 | \tContact with heat and hot substances |\n”,

“| X20-X29 | \tContact with venomous animals and plants |\n”,

“| X30-X39 | \tExposure to forces of nature |\n”,

“| X40-X49 | \tAccidental poisoning by and exposure <br> to noxious substances |\n”,

“| X50-X57 | \t\”Overexertion, travel and privation\” |\n”,

“| X58-X59 | \tAccidental exposure to other and<br> unspecified factors |”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“## Analysis & Discussion [70 marks]”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“This section should provide analysis and discussion to address the following questions:\n”,

”    \n”,

“- a. What are the current leading causes of injury deaths in your country?  \n”,

“- i. Provide a pie chart, which displays the top 5 leading causes of deaths due to injuries and violence based on your country’s most current year’s data.  Include the remainder of the injury and violence deaths as other.  \n”,

“- b. Have injury deaths risen in rank over the last thirty years?  \n”,

“- i. Provide tables which compare the top twenty causes of all deaths (not just due to injuries and violence) over the last thirty years, in fifteen year increments starting from the most current year’s data and going backwards.  For example, if your country has data from 1950 to 2010 you will have a table displaying the top 20 causes of all deaths for the years 1980, 1995 and 2010. \n”,

“- ii. Provide a time series chart depicting how the top ten current leading causes of injury and deaths have changed over the last thirty years.  Your data should be in five year increments.  For example, if your country has data from 1950 to 2010, your chart will include the years:  1980, 1985, 1990, 1995, 2000, 2005 and 2010.\n”,

“- c. Are some groups more vulnerable to injuries and violence than others?   \n”,

“- i. Provide a vertical bar chart which displays the death rates by cause of injury and group for the most current year’s data.  Use the top five current leading causes of injury and deaths (from part b), and the following groups: youths (ages 15-29, any gender), males (all ages) and females (all ages).   \n”,

“- d. Does poverty increase the risk of injury? \n”,

“- i. Compare your country’s deaths due to injury and violence to another country in your world region which has a different WHO income level classification.  For example, per the provided LMIC-HIC_country_grouping document, Australia’s WHO region code is Wpr HI, indicating that Australia is in the West pacific region and is considered High income.  So a good comparison country for Australia could be Vanuatu, with a WHO region of Wpr LMI. For each of your countries, Provide an appropriate chart with the top twenty causes of all deaths (not just due to injuries and violence) based on the most recent years data which is available for both countries.  For example, if the most recent year’s data for one country is 2011, and the other’s is 2009, you should use the 2009 data for both countries.\n”,

“- ii. Provide a vertical bar chart displaying the percentage of all deaths due to injury and violence for both countries. ”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“Provide a pie chart, which displays the top 5 leading causes of deaths due to injuries and violence based on your country’s most current year’s data.  Include the remainder of the injury and violence deaths as other.”

]

},

{

“cell_type”: “code”,

“execution_count”: 35,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: [

“injury_mask = ((target_morticd[‘Cause’] > ‘R99’) & (target_morticd[‘Cause’] < ‘T15’)) | ((target_morticd[‘Cause’] > ‘T99’) & (target_morticd[‘Cause’] < ‘W60’))\n”,

“injury_causes = target_morticd[injury_mask& (target_morticd[‘Year’] == curr_year)]\n”,

“injury_causes = injury_causes.iloc[:, 8].fillna(0.0).groupby(injury_causes[‘Cause’]).sum()”

]

},

{

“cell_type”: “code”,

“execution_count”: 37,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: [

“top5_injury_causes = injury_causes.nlargest(5)\n”,

“top5_injury_causes\n”,

“\n”,

“\n”,

“top5 = all_codes.join(top5_injury_causes.to_frame(), how=’right’)\n”,

“top5.loc[‘V00’] = [‘Other’, injury_causes[~injury_causes.index.isin(top5_injury_causes.index)].sum()]\n”,

“top5 = top5.rename(columns={‘Deaths1’: ‘Value’})”

]

},

{

“cell_type”: “code”,

“execution_count”: 38,

“metadata”: {},

“outputs”: [

{

“name”: “stdout”,

“output_type”: “stream”,

“text”: [

“\n”

]

},

{

“data”: {

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xmX1DULz2SIt/h5l9amaLCYPv7zSifG40sxlmNj/eY2PiJu+nDrg0ltdiwsD4IjObZmY1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\nkiRJ0swwxEqSJEmSZsavMbhQYIXPEjMAAAAASUVORK5CYII=\n”,

“text/plain”: [

“<matplotlib.figure.Figure at 0x7fcc5c7fe518>”

]

},

“metadata”: {},

“output_type”: “display_data”

}

],

“source”: [

“patches, texts = pl.pie(top5[‘Value’], labels=top5.index)\n”,

“pl.axis(‘equal’)\n”,

“pl.legend(patches, list(top5[‘ICD Title’].values), bbox_to_anchor=(2.8, 1), frameon=False)\n”,

“print()”

]

},

{

“cell_type”: “code”,

“execution_count”: 40,

“metadata”: {},

“outputs”: [

{

“data”: {

“text/html”: [

“<div>\n”,

“<style>\n”,

”    .dataframetheadtr:only-childth {\n”,

”        text-align: right;\n”,

”    }\n”,

“\n”,

”    .dataframetheadth {\n”,

”        text-align: left;\n”,

”    }\n”,

“\n”,

”    .dataframetbodytrth {\n”,

”        vertical-align: top;\n”,

”    }\n”,

“</style>\n”,

“<table border=\”1\” class=\”dataframe\”>\n”,

”  <thead>\n”,

”    <tr style=\”text-align: right;\”>\n”,

”      <th></th>\n”,

”      <th>index</th>\n”,

”      <th>ICD Title</th>\n”,

”      <th>2003</th>\n”,

”      <th>2008</th>\n”,

”      <th>2013</th>\n”,

”    </tr>\n”,

”  </thead>\n”,

”  <tbody>\n”,

”    <tr>\n”,

”      <th>0</th>\n”,

”      <td>I25</td>\n”,

”      <td>Chronic ischemic heart disease</td>\n”,

”      <td>7869</td>\n”,

”      <td>9288</td>\n”,

”      <td>9607</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>1</th>\n”,

”      <td>I64</td>\n”,

”      <td>Stroke, not specified as hemorrhage or infarction</td>\n”,

”      <td>3957</td>\n”,

”      <td>4069</td>\n”,

”      <td>3596</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>2</th>\n”,

”      <td>K74</td>\n”,

”      <td>Fibrosis and cirrhosis of liver</td>\n”,

”      <td>1489</td>\n”,

”      <td>1977</td>\n”,

”      <td>1921</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>3</th>\n”,

”      <td>I21</td>\n”,

”      <td>Acute myocardial infarction</td>\n”,

”      <td>929</td>\n”,

”      <td>969</td>\n”,

”      <td>1159</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>4</th>\n”,

”      <td>J44</td>\n”,

”      <td>Other chronic obstructive pulmonary disease</td>\n”,

”      <td>1826</td>\n”,

”      <td>1452</td>\n”,

”      <td>981</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>5</th>\n”,

”      <td>P22</td>\n”,

”      <td>Respiratory distress of newborn</td>\n”,

”      <td>177</td>\n”,

”      <td>593</td>\n”,

”      <td>725</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>6</th>\n”,

”      <td>V89</td>\n”,

”      <td>Motor- or nonmotor-vehicle accident, type of v…</td>\n”,

”      <td>410</td>\n”,

”      <td>629</td>\n”,

”      <td>618</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>7</th>\n”,

”      <td>J18</td>\n”,

”      <td>Pneumonia, organism unspecified</td>\n”,

”      <td>1037</td>\n”,

”      <td>782</td>\n”,

”      <td>614</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>8</th>\n”,

”      <td>C16</td>\n”,

”      <td>Malignant neoplasm of stomach</td>\n”,

”      <td>544</td>\n”,

”      <td>586</td>\n”,

”      <td>599</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>9</th>\n”,

”      <td>I67</td>\n”,

”      <td>Other cerebrovascular diseases</td>\n”,

”      <td>1176</td>\n”,

”      <td>1029</td>\n”,

”      <td>562</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>10</th>\n”,

”      <td>C34</td>\n”,

”      <td>Malignant neoplasm of bronchus and lung</td>\n”,

”      <td>443</td>\n”,

”      <td>349</td>\n”,

”      <td>476</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>11</th>\n”,

”      <td>I11</td>\n”,

”      <td>Hypertensive heart disease</td>\n”,

”      <td>384</td>\n”,

”      <td>425</td>\n”,

”      <td>430</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>12</th>\n”,

”      <td>X70</td>\n”,

”      <td>Intentional self-harm (suicide) by hanging, st…</td>\n”,

”      <td>415</td>\n”,

”      <td>419</td>\n”,

”      <td>414</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>13</th>\n”,

”      <td>A15</td>\n”,

”      <td>NaN</td>\n”,

”      <td>485</td>\n”,

”      <td>372</td>\n”,

”      <td>329</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>14</th>\n”,

”      <td>P21</td>\n”,

”      <td>Birth asphyxia</td>\n”,

”      <td>180</td>\n”,

”      <td>546</td>\n”,

”      <td>316</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>15</th>\n”,

”      <td>X45</td>\n”,

”      <td>Accidental poisoning by and exposure to alcohol</td>\n”,

”      <td>354</td>\n”,

”      <td>355</td>\n”,

”      <td>275</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>16</th>\n”,

”      <td>A16</td>\n”,

”      <td>Respiratory tuberculosis, not confirmed bacter…</td>\n”,

”      <td>374</td>\n”,

”      <td>194</td>\n”,

”      <td>223</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>17</th>\n”,

”      <td>N03</td>\n”,

”      <td>Chronic nephritic syndrome</td>\n”,

”      <td>345</td>\n”,

”      <td>282</td>\n”,

”      <td>199</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>18</th>\n”,

”      <td>I61</td>\n”,

”      <td>Intracerebral hemorrhage</td>\n”,

”      <td>743</td>\n”,

”      <td>425</td>\n”,

”      <td>192</td>\n”,

”    </tr>\n”,

”    <tr>\n”,

”      <th>19</th>\n”,

”      <td>R54</td>\n”,

”      <td>Senility</td>\n”,

”      <td>485</td>\n”,

”      <td>195</td>\n”,

”      <td>34</td>\n”,

”    </tr>\n”,

”  </tbody>\n”,

“</table>\n”,

“</div>”

],

“text/plain”: [

”   index                                          ICD Title  2003  2008  2013\n”,

“0    I25                     Chronic ischemic heart disease  7869  9288  9607\n”,

“1    I64  Stroke, not specified as hemorrhage or infarction  3957  4069  3596\n”,

“2    K74                    Fibrosis and cirrhosis of liver  1489  1977  1921\n”,

“3    I21                        Acute myocardial infarction   929   969  1159\n”,

“4    J44        Other chronic obstructive pulmonary disease  1826  1452   981\n”,

“5    P22                    Respiratory distress of newborn   177   593   725\n”,

“6    V89  Motor- or nonmotor-vehicle accident, type of v…   410   629   618\n”,

“7    J18                    Pneumonia, organism unspecified  1037   782   614\n”,

“8    C16                      Malignant neoplasm of stomach   544   586   599\n”,

“9    I67                     Other cerebrovascular diseases  1176  1029   562\n”,

“10   C34            Malignant neoplasm of bronchus and lung   443   349   476\n”,

“11   I11                         Hypertensive heart disease   384   425   430\n”,

“12   X70  Intentional self-harm (suicide) by hanging, st…   415   419   414\n”,

“13   A15                                                NaN   485   372   329\n”,

“14   P21                                     Birth asphyxia   180   546   316\n”,

“15   X45    Accidental poisoning by and exposure to alcohol   354   355   275\n”,

“16   A16  Respiratory tuberculosis, not confirmed bacter…   374   194   223\n”,

“17   N03                       Chronic nephritic syndrome     345   282   199\n”,

“18   I61                           Intracerebral hemorrhage   743   425   192\n”,

“19   R54                                           Senility   485   195    34”

]

},

“execution_count”: 40,

“metadata”: {},

“output_type”: “execute_result”

}

],

“source”: [

“years = [curr_year-10, curr_year-5, curr_year]\n”,

“morticd20 = target_morticd[target_morticd[‘Year’].isin(years) &target_morticd[‘Cause’].isin(top20.index)]\n”,

“\n”,

“m20 = morticd20.iloc[:, 8].fillna(0.0).groupby([morticd20[‘Cause’], morticd20[‘Year’]]).sum().unstack()\n”,

“all_codes.join(m20, how=’right’).sort_values(by=2013,ascending=False).reset_index()”

]

},

{

“cell_type”: “code”,

“execution_count”: 41,

“metadata”: {},

“outputs”: [

{

“data”: {

“text/plain”: [

“<matplotlib.text.Text at 0x7fcc591d0550>”

]

},

“execution_count”: 41,

“metadata”: {},

“output_type”: “execute_result”

},

{

“data”: {

“image/png”: 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aKXVkZ8Nll+lpt6NG+ToawzAM/9KYxGEVkVc9HomXFBfrpPF//weTJvk6GsMw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\nrkJggg==\n”,

“text/plain”: [

“<matplotlib.figure.Figure at 0x7fcc5d030668>”

]

},

“metadata”: {},

“output_type”: “display_data”

}

],

“source”: [

“top10_injury_causes = injury_causes.nlargest(10)\n”,

“injury_causes2 = target_morticd[injury_mask]\n”,

“injury_causes2.loc[injury_causes2.index.isin(top10_injury_causes.index)]\n”,

“m10 = injury_causes2.iloc[:, 8].groupby([injury_causes2[‘Cause’], injury_causes2[‘Year’]]).sum().unstack()\n”,

“m10 = m10.loc[m10.index.isin(top10_injury_causes.index)].fillna(0.0)\n”,

“#all_codes.join(m10, how=’right’).reset_index()\n”,

“for index, row in m10.iterrows():\n”,

”    pl.plot(m10.columns, row)\n”,

“pl.xlabel(\”Year\”)\n”,

“pl.ylabel(\”Number of Deaths\”)”

]

},

{

“cell_type”: “code”,

“execution_count”: 64,

“metadata”: {},

“outputs”: [

{

“data”: {

“text/plain”: [

“<matplotlib.legend.Legend at 0x7fcc5d0d8240>”

]

},

“execution_count”: 64,

“metadata”: {},

“output_type”: “execute_result”

},

{

“data”: {

“image/png”: 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“text/plain”: [

“<matplotlib.figure.Figure at 0x7fcc5d7d8320>”

]

},

“metadata”: {},

“output_type”: “display_data”

}

],

“source”: [

“years = [curr_year-10, curr_year-5, curr_year]\n”,

“morticd20 = target_morticd[(target_morticd[‘Year’] == curr_year) &target_morticd[‘Cause’].isin(top5.index)]\n”,

“\n”,

“mgender = morticd20.iloc[:, 8].fillna(0.0).groupby([morticd20[‘Cause’], morticd20[‘Sex’]]).sum().unstack()\n”,

“\n”,

“mgender[3] = morticd20.iloc[:, 16:19].fillna(0.0).sum(axis=1).groupby(morticd20[‘Cause’]).sum()\n”,

“mgender = mgender.rename(columns={1: ‘Male’, 2:’Female’, 3: ‘Youths’}).reset_index()\n”,

“\n”,

“width=0.3\n”,

“pl.bar(mgender.index + width, mgender[‘Youths’], width, color=’r’, label=’Youths’)\n”,

“pl.bar(mgender.index + 2*width, mgender[‘Male’], width, color=’g’, label=’Male’)\n”,

“pl.bar(mgender.index + 3*width, mgender[‘Female’], width, color=’b’, label=’Female’)\n”,

“pl.legend(loc=’best’, )”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“## Conclusion ”

]

},

{

“cell_type”: “code”,

“execution_count”: null,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: []

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“## References”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“- https://www.cia.gov/library/publications/the-world-factbook/geos/kg.html\n”,

“- http://www.who.int/countries/kgz/en/\n”,

“- https://en.wikipedia.org/wiki/Kyrgyzstan\n”

]

},

{

“cell_type”: “markdown”,

“metadata”: {},

“source”: [

“## Appendices ”

]

},

{

“cell_type”: “code”,

“execution_count”: null,

“metadata”: {

“collapsed”: true

},

“outputs”: [],

“source”: []

}

],

“metadata”: {

“kernelspec”: {

“display_name”: “Python 3”,

“language”: “python”,

“name”: “python3”

},

“language_info”: {

“codemirror_mode”: {

“name”: “ipython”,

“version”: 3

},

“file_extension”: “.py”,

“mimetype”: “text/x-python”,

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